In sedimentary petrography, small-scale samples of reservoir rocks, such as the sections, peels, and slabs, are typically analyzed and studied. An early objective of the study of such samples was the deduction of the characteristics of the sediment shortly after deposition.
Carbonates recrystallize much more readily and pervasively than detrital sandstones. As a result, an awareness of an accessible record of post depositional history came earlier to carbonate petrologists than to sandstone petrologists. Interest in diagenetic state and history increased as it became clear that much porosity in petroleum reservoirs, both carbonate and detrital, is secondary and also that diagenetic mineral phases growing on pore walls could adversely affect hydrocarbon recovery. Intensive research on reservoir quality using thin section and scanning electron microscope (SEM) imagery, together with complementary geochemical/isotropic data, has moved this part of the study of diagenesis from an area of speculation to the presently applied science of reservoir assessment.
Petrologists have come to treat pores as compositional phases. This has more than pragmatic justification. Pores are not mere voids, but signify the occurrence of a fluid or gaseous phase. A pore/pore wall interface possesses surface energy exactly in the same way as a quartz/feldspar interface; growth or loss of pores can be thought to operate under the dynamic/kinetic parameters which affect the stability of the surrounding solid phases. Indeed, in order to define a general measure of the extent and direction of diagenesis of a rock unit, measurements of pore characteristics can serve as a first order diagenetic variable.
Permeability in reservoir rocks occurs through a three-dimensional interconnected pore network. Conventionally, the wider parts of the network are termed "pores" and the narrower parts are termed "pore throats". The three-dimensionality of the pore complex has been directly observed by dissolving the rock matrix to leave an epoxy-impregnated framework.
Most observations of pores are from thin sections or SEM imagery which provide limited direct three-dimensional information. In reservoir studies, it is important that three-dimensional information concerning the pore complex be developed for an understanding of fluid flow and its correlation with petrophysical data such as capillary pressure curves and wire line log response. What is desired is a quantitative variable or variables derived from the two-dimensional pore complex which can then be correlated with petrophysical and geophysical measurements as well as with the response of the reservoir to production.
It is assumed that the pore system displayed on an essentially two-dimensional slice bears some relationship to the three-dimensional network from which it was extracted. Direct extrapolation from two-dimensional observations to the third dimension has not been achieved and may never be achieved without simplifying assumptions, e.g., spherical, hexagonally packed, grains. There is little doubt, however, that there must be some relationship, termed a "transfer function", between the pore complex intersected by a plane and the three-dimensional network. It is thus an assumption of the present application that significant changes in the three-dimensional network are reflected in changes in the two-dimensional section.
Sedimentary petrography represents a discipline which concerns analysis of micro-scaled imagery of sedimentary rocks. The data necessary to characterize pore-complex geometry in a single field of view generally is most expeditiously developed through computer-assisted analysis of the images. The general field of image analysis is relatively mature so that general principles and strategies have already been defined. That such an approach is required for pore complex analysis has been realized for more than a decade.
Image analysis requires an image acquisition system consisting of (1) a sensor such as a videoscanner, (2) an analog/digital converter to convert the analog television signal to digital form, and (3) a data processor. In the data processor, the digital representation of the scene is electronically arranged into an array of grid points or pixels. Each pixel is defined by three values: two spatial coordinates (X, Y) and a "gray level" intensity value. The gray level, a measure of brightness, is restricted to integral values. Because the pixels form a grid, once the grid spacing is known, the coordinates of each pixel are known implicitly by knowing the location of one pixel in the array. In the system of the present invention, three scenes of the same field of view are digitized through red, green and blue filters respectively. These three "color planes" when combined will produce a complete color image.
One of the main objectives in image analysis is image segmentation. Segmentation is the determination of which pixels in the array belong to the same category. For instance, an algorithm which subdivides a thin section image into the categories "quartz" and "others" necessarily accomplishes a segmentation with respect to quartz.
In the segmentation of pores, advantage is taken of the fact that piror to sectioning, the rocks can be impregnated with pigmented, typically, blue, epoxy. Because few, if any, constituents in reservoir rocks are naturally colored blue, segmentation can be achieved through a digital filter. A filter may consist of the average ratio of gray-level intensities from each color plane of red to green to blue of pixels located in pores. An image processing algorithm then compares that ratio with that of every pixel, and assigns, for example, a value of "one" to those with the "correct" ratio for pores and a "zero" for all others. The result is a binary image which ensures that in the subsequent analysis of pore geometry only the pores will be analyzed.
With the pore-complex identified, analysis of porosity can commence. Porosity is the proportion of pore pixels to total pixels in the scene. The porosity value estimated in this way is not the same as porosity as measured by physical tests. Pores are measured by the presence of blue-dyed epoxy impregnation. Thus, the porosity defined by petrographic image analysis is more closely linked to effective or interconnected porosity than to total porosity.
Most minerals in sedimentary rocks are transparent to translucent and this characteristic can in some cases affect porosity estimates. As the illumination level increases, more and more blue-dyed pores can be seen through mineral grains. Thus, increasing proportions of pores inclined or even parallel to the plane of section will be detected. The problem can be minimized by careful control of illumination and adjustment of the values of the digital filter.
Total pore perimeter is another property that is evaluated. This is an especially useful variable in that it has been shown that total pore perimeter per unit area is directly proportional to pore surface area per unit volume. The ratio of total pore area to the total pore perimeter can provide information concerning pore roughness or tortuosity.
Another variable tied to roughness/tortuosity is bending energy. It has been pointed out that perimeter measured from pixel to pixel along a periphery may deviate significantly from perimeter measured continuously. Bending energy, representing the energy necessary to deform a circle into the shape of the pore, is defined as the normalized sum of squares of curvature of the vertices of the periphery.
Bending energy can be calculated on a pore-by-pore basis. When summed or averaged, the pore measurements can be a global measure. Considering the fact that pores have quite complex geometries, bending energy is in fact a more generally useful variable than simpler measures of geometry such as measurements of long and intermediate axes. However, pores of many shapes can yield equivalent values of bending energy. What is needed in many cases is a way to measure subfeatures of a pore. It has been recognized that often pores can possess extended complex geometries and so conventional shape measurement variables would often be of little value. One prior art solution was to develop an algorithm that would subdivide the pore imagery into subdivisions of relatively simple geometry--each of which would then be efficiently evaluated by conventional shape and size variables.